1 R Markdown - Week 2 Assignment

This is an R Markdown document. It will be my first attempt at the Week 2 Live assignment constructed and commented solely in RMarkdown. I intend to restate each question within the assignment and provide the code within the document. I also am going to practice my GitHub skills by creating a repo for this assignment. My GitHub repo is CTG-SMU-DataHub. I’ll use “knitr” to knit the questions together to make one coherent set.

1.1 Bullet Point 1 - Use the PlayerBBall.csv dataset to visually represent (summarize) the number of players in each position.

First, I will load the PlayerBBall.csv into RStudio. Taking a peak at the data set with MS Excel, there are 4550 observations (rows) and 8 columns and the data set contain headings. It would seem that a bar chart with the positions on the x-axis makes sense and then a simple count. After reading in the data set and giving it a variable name, ggplot function within the ggplot2 package will allow this scheme to come alive.

p <- read.csv("/Users/tgarn/OneDrive/Desktop/SMU - MS Data Science/Courses/GitHub/DS_6306_weekly_assignments/Week_2/PlayersBBall.csv", header = TRUE)

library(ggplot2)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
p %>% ggplot(mapping = aes(x = position)) + geom_bar(stat = "count")

This provides the bar chart, as expected, but the number is only an approximation. ggplotly would provide an interactive means to bring specificity to this answer.

library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
library(ggthemes)

z = p %>% ggplot(mapping = aes(x = position)) + geom_bar(stat = "count") + theme_economist() + labs(xlab = "position", ylab = "frequency", title = "Players in each position")
                                                                                                

ggplotly(z)

This is a much more satisfying interactive chart that provides an exact count of each position.

1.2 Bullet 2 - Use the dataset to visually investigate the distribution of the weight of centers (C) is greater than the distribution of the weight of forwards (F).

There are likely numerous ways to investigate the distribution of the weights of centers (C) and forward (F), but the most straight forward is the boxplot, as show below.

p %>% ggplot(aes(x = position, y = weight)) + geom_boxplot() + theme_economist() + labs(xlab = "position", ylab = "weight in pounds", title = "Distribution of weight by position")
## Warning: Removed 6 rows containing non-finite values (`stat_boxplot()`).

This answers the question, but it leaves me wanting a better visual solution. A solution that: 1. Isolates centers and forwards 2. Quantifies the weight distribution for each position

I will devote a half hour to seeking a solution.

First, to isolate the centers and forwards from the rest of the positions. Then, take the weight of the centers and then the weight of the forwards.

C = p[p$position == "C",]
Forw = p[p$position == "F",]

WC = .colMeans(C$weight, length(C), 1, na.rm = FALSE)
WF = .colMeans(Forw$weight, length(Forw), 1, na.rm = FALSE)

paste("The average weight of the centers is,", WC,"pounds, and the average weight of the forwards is,", WF,"pounds.")
## [1] "The average weight of the centers is, 245.5 pounds, and the average weight of the forwards is, 221 pounds."

I recognize that although numerically pleasing, it doesn’t specifically address the issue of “visualizing” the difference in weights, but the distribution of weights is shown visually via the box plot. I’m hoping that the combined solution will satisfy the client.

1.3 Bullet 3 - Use the dataset to visually investigate if the distribution of the height of centers (C) is greater than the distribution of the height of forwards (F).

This should be fairly straight forward (said the man headed to the gallows). I will repeat the Bullet 2 process. First, I’ll refresh the dataset. The operation will be to separate the feet and inches and convert feet to inches so we can have uniformity. First, the height column is a character. The task is to either split the characters of feet and inches into two separate columns, get rid of the “-” in between and then convert to numeric and then recreate the height column with units of inches. Or, the same operation, but first converting to numeric and then the rest. My guess is that the first method will be the way to go and that’s what I’m going to attack.

After spending way too much time trying to wrangle the data via RStudio, I took another approach which was to modify the data in Excel to inches. This is probably not the intended solution, but it’s hard to argue with results. And, if this were a client, I don’t think they’d care how you arrived at the solution. The DS6306 professor, on the other hand, may have something to say about this. But, I’ve been transparent with my methods.

After importing the new dataset, I will address the question posed in bullet 3.

j <- read.csv("/Users/tgarn/OneDrive/Desktop/SMU - MS Data Science/Courses/GitHub/DS_6306_weekly_assignments/Week_2/DS6306_week2/PlayersBBall.csv", header = TRUE)

j %>% ggplot(aes(x = position, y = height)) + geom_boxplot() + theme_economist() + labs(x = "position", y = "height in inches", title = "Player height by position")

We can see from the boxplot that the distribution of the Centers is greater than the distribution of the Forwards.

1.4 Bullet 4 - Use the dataset to visually investigate if the distribution of height is different between any of the positions.

Once again, we utilize the boxplot to visually see that each of the position’s heights are differently distributed.

j <- read.csv("/Users/tgarn/OneDrive/Desktop/SMU - MS Data Science/Courses/GitHub/DS_6306_weekly_assignments/Week_2/DS6306_week2/PlayersBBall.csv", header = TRUE)

j %>% ggplot(aes(x = position, y = height, color = position)) + geom_boxplot() + theme_economist() + xlab("Position") + ylab("height in inches") + labs(title = "Distribution of player height by position")

1.5 Bullet 5 - Use the dataset to investigate how the player’s height is related to the player’s weight. How does height change as the weight changes?

I would imagine that regression would be helpful but could be as simple as correlation. Let’s give is a shot.

j <- read.csv("/Users/tgarn/OneDrive/Desktop/SMU - MS Data Science/Courses/GitHub/DS_6306_weekly_assignments/Week_2/DS6306_week2/PlayersBBall.csv", header = TRUE)

x = data.frame(j$height)
head(x)
##   j.height
## 1       82
## 2       81
## 3       86
## 4       73
## 5       78
## 6       81
y = data.frame(j$weight)
head(y)
##   j.weight
## 1      240
## 2      235
## 3      225
## 4      162
## 5      223
## 6      225
AA = cor(x, y)
head(AA)
##          j.weight
## j.height       NA
lm(height~weight, j)
## 
## Call:
## lm(formula = height ~ weight, data = j)
## 
## Coefficients:
## (Intercept)       weight  
##     54.0138       0.1152
summary(lm(height~weight, j))
## 
## Call:
## lm(formula = height ~ weight, data = j)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.063 -1.334 -0.063  1.327 13.937 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 54.013826   0.244593  220.83   <2e-16 ***
## weight       0.115246   0.001162   99.21   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.057 on 4542 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.6842, Adjusted R-squared:  0.6842 
## F-statistic:  9842 on 1 and 4542 DF,  p-value: < 2.2e-16
ggplot(j, aes(x = height, y = weight)) + geom_point() + stat_smooth(method = "lm") + labs(x = "height in inches", y = "weight in pounds", title = "relationship of players weight and height") + theme_economist()
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 6 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 6 rows containing missing values (`geom_point()`).

From the data and the plot, we can see that weight and height are positively correlated. The equation is: ### Height = 54.013286” + 0.115246*(Weight)”

1.6 Bullet 6 - Is there any difference in the relationship between height and weight between positions? Are height and weight related differently for different positions?

To accomplish this, I first need to separate the positions into unique datasets for each position. Then, perform the same regression analysis as the prior question. Since there are 7 separate positions, this would make an untenable number of iterations from which to analyze each position against all the others. Perhaps splitting into 7 different dataframes and performing a linear regression on each set would suffice. The linear equation results for each could be analyzed against the others. The resultant equation for each position could then be plotted against the others.

First, the create 7 new datasets for C, C-F, F, F-C, C-F, G-F and G.

j <- read.csv("/Users/tgarn/OneDrive/Desktop/SMU - MS Data Science/Courses/GitHub/DS_6306_weekly_assignments/Week_2/DS6306_week2/PlayersBBall.csv", header = TRUE)


data_C <- j[j$position == "C",]
head(data_C)
##                   name year_start year_end position height weight birth_date
## 3  Kareem Abdul-Jabbar       1970     1989        C     86    225  16-Apr-47
## 22        Steven Adams       2014     2018        C     85    255  20-Jul-93
## 33       Alexis Ajinca       2009     2017        C     86    248   6-May-88
## 36       Solomon Alabi       2011     2012        C     85    251  21-Mar-88
## 38         Gary Alcorn       1960     1961        C     81    225   8-Oct-36
## 40        Cole Aldrich       2011     2018        C     83    250  31-Oct-88
##                                  college
## 3  University of California, Los Angeles
## 22              University of Pittsburgh
## 33                                      
## 36              Florida State University
## 38   California State University, Fresno
## 40                  University of Kansas
data_C_F <- j[j$position == "C-F",]
head(data_C_F)
##                name year_start year_end position height weight birth_date
## 2   Zaid Abdul-Aziz       1969     1978      C-F     81    235   7-Apr-46
## 16      Alvan Adams       1976     1988      C-F     81    210  19-Jul-54
## 24      Bam Adebayo       2018     2018      C-F     82    243  18-Jul-97
## 34       Henry Akin       1967     1969      C-F     82    225  31-Jul-44
## 48 Victor Alexander       1992     2002      C-F     81    265  31-Aug-69
## 50       Bill Allen       1968     1968      C-F     80    205           
##                        college
## 2        Iowa State University
## 16      University of Oklahoma
## 24      University of Kentucky
## 34   Morehead State University
## 48       Iowa State University
## 50 New Mexico State University
data_F <- j[j$position == "F",]
head(data_F)
##                   name year_start year_end position height weight birth_date
## 5    Tariq Abdul-Wahad       1998     2003        F     78    223   3-Nov-74
## 6  Shareef Abdur-Rahim       1997     2008        F     81    225  11-Dec-76
## 7        Tom Abernethy       1977     1981        F     79    220   6-May-54
## 9       John Abramovic       1947     1948        F     75    195   9-Feb-19
## 14           Bud Acton       1968     1968        F     78    210  11-Jan-42
## 15          Quincy Acy       2013     2018        F     79    240   6-Oct-90
##                           college
## 5       San Jose State University
## 6        University of California
## 7              Indiana University
## 9  Salem International University
## 14              Hillsdale College
## 15              Baylor University
data_F_C <- j[j$position == "F-C",]
head(data_F_C)
##                 name year_start year_end position height weight birth_date
## 1     Alaa Abdelnaby       1991     1995      F-C     82    240  24-Jun-68
## 13        Mark Acres       1988     1993      F-C     83    220  15-Nov-62
## 39    Furkan Aldemir       2015     2015      F-C     82    240   9-Aug-91
## 41 LaMarcus Aldridge       2007     2018      F-C     83    260  19-Jul-85
## 52     Jarrett Allen       2018     2018      F-C     82    234  21-Apr-98
## 55       Lavoy Allen       2012     2017      F-C     81    260   4-Feb-89
##                          college
## 1                Duke University
## 13       Oral Roberts University
## 39                              
## 41 University of Texas at Austin
## 52 University of Texas at Austin
## 55             Temple University
data_C_F <- j[j$position == "C-F",]
head(data_C_F)
##                name year_start year_end position height weight birth_date
## 2   Zaid Abdul-Aziz       1969     1978      C-F     81    235   7-Apr-46
## 16      Alvan Adams       1976     1988      C-F     81    210  19-Jul-54
## 24      Bam Adebayo       2018     2018      C-F     82    243  18-Jul-97
## 34       Henry Akin       1967     1969      C-F     82    225  31-Jul-44
## 48 Victor Alexander       1992     2002      C-F     81    265  31-Aug-69
## 50       Bill Allen       1968     1968      C-F     80    205           
##                        college
## 2        Iowa State University
## 16      University of Oklahoma
## 24      University of Kentucky
## 34   Morehead State University
## 48       Iowa State University
## 50 New Mexico State University
data_G_F <- j[j$position == "G-F",]
head(data_G_F)
##               name year_start year_end position height weight birth_date
## 10    Alex Abrines       2017     2018      G-F     78    190   1-Aug-93
## 60      Tony Allen       2005     2018      G-F     76    213  11-Jan-82
## 76   Alan Anderson       2006     2017      G-F     78    220  16-Oct-82
## 79  Cliff Anderson       1968     1971      G-F     74    200   7-Sep-44
## 87  James Anderson       2011     2016      G-F     78    213  25-Mar-89
## 99 Willie Anderson       1989     1997      G-F     79    190   8-Jan-67
##                      college
## 10                          
## 60 Oklahoma State University
## 76 Michigan State University
## 79 Saint Joseph's University
## 87 Oklahoma State University
## 99     University of Georgia
data_G <- j[j$position == "G",]
head(data_G)
##                  name year_start year_end position height weight birth_date
## 4  Mahmoud Abdul-Rauf       1991     2001        G     73    162   9-Mar-69
## 8         Forest Able       1957     1957        G     75    180  27-Jul-32
## 11         Alex Acker       2006     2009        G     77    185  21-Jan-83
## 12       Don Ackerman       1954     1954        G     73    183   4-Sep-30
## 19       Hassan Adams       2007     2009        G     76    220  20-Jun-84
## 20       Jordan Adams       2015     2016        G     77    209   8-Jul-94
##                                  college
## 4             Louisiana State University
## 8            Western Kentucky University
## 11                 Pepperdine University
## 12                Long Island University
## 19                 University of Arizona
## 20 University of California, Los Angeles

Now, I’ll run the linear regression and summary function for each position with relationship defined between height and weight.

1.6.1 For Centers

summary(lm(height~weight, data_C))
## 
## Call:
## lm(formula = height ~ weight, data = data_C)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2772 -1.2772 -0.0589  1.3298  9.4693 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 72.79823    0.94063   77.39   <2e-16 ***
## weight       0.04366    0.00383   11.40   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.912 on 498 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.2069, Adjusted R-squared:  0.2053 
## F-statistic: 129.9 on 1 and 498 DF,  p-value: < 2.2e-16

1.6.2 For Centers: Height = 72.79823” + 0.04366*(Weight)”

1.6.3 For Center-Forwards

summary(lm(height~weight, data_C_F))
## 
## Call:
## lm(formula = height ~ weight, data = data_C_F)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.6015 -1.1576 -0.0461  1.1762  6.4874 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 71.376293   1.600234  44.604  < 2e-16 ***
## weight       0.044458   0.006967   6.381 1.05e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.835 on 217 degrees of freedom
## Multiple R-squared:  0.158,  Adjusted R-squared:  0.1541 
## F-statistic: 40.72 on 1 and 217 DF,  p-value: 1.047e-09

1.6.4 For Center-Forwards: Height = 71.376293” + 0.044458*(Weight)”

1.6.5 For Forwards:

summary(lm(height~weight, data_F))
## 
## Call:
## lm(formula = height ~ weight, data = data_F)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1828 -1.0434  0.1195  1.1195  5.2473 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 66.670252   0.530859  125.59   <2e-16 ***
## weight       0.058144   0.002425   23.98   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.631 on 1287 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.3088, Adjusted R-squared:  0.3083 
## F-statistic: 575.1 on 1 and 1287 DF,  p-value: < 2.2e-16

1.6.6 For Forwards: Height = 66.670252” + 0.058144*(Weight)”

1.6.7 For Forward-Centers:

summary(lm(height~weight, data_F_C))
## 
## Call:
## lm(formula = height ~ weight, data = data_F_C)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3211 -1.2218  0.0752  1.2040  5.4813 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 67.263795   1.050507   64.03   <2e-16 ***
## weight       0.059396   0.004674   12.71   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.769 on 386 degrees of freedom
## Multiple R-squared:  0.295,  Adjusted R-squared:  0.2931 
## F-statistic: 161.5 on 1 and 386 DF,  p-value: < 2.2e-16

1.6.8 For Forward-Centers: Height = 67.263795” + 0.059396*(Weight)”

1.6.9 For Guards:

summary(lm(height~weight, data_G))
## 
## Call:
## lm(formula = height ~ weight, data = data_G)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.7013 -0.9421  0.0432  1.0284  5.0579 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 56.502390   0.510113  110.76   <2e-16 ***
## weight       0.097051   0.002721   35.67   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.613 on 1570 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.4476, Adjusted R-squared:  0.4473 
## F-statistic:  1272 on 1 and 1570 DF,  p-value: < 2.2e-16

1.6.10 For Guards: Height = 56.502390” + 0.097051*(Weight)”

1.6.11 For Guard-Forwards:

summary(lm(height~weight, data_G_F))
## 
## Call:
## lm(formula = height ~ weight, data = data_G_F)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1775 -1.0893  0.0014  1.1802  5.0014 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 58.697623   1.123516   52.24   <2e-16 ***
## weight       0.091058   0.005668   16.06   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.656 on 358 degrees of freedom
## Multiple R-squared:  0.4189, Adjusted R-squared:  0.4173 
## F-statistic: 258.1 on 1 and 358 DF,  p-value: < 2.2e-16

1.6.12 For Guard-Forwards: Height = 58.697623” + 0.097051*(Weight)”

1.6.13 Each position has a different linear equation describing the relationship of the height/weight of each. Therefore, we can say that there is a different relationship amongst the different positions.

1.7 Bullet 7 - A historian would like to investigae the claim that the heights of players have increased over the years. Analyse this claim graphically/visually.

For this question, we need to plot the years versus the heights as a scatter plot and draw a trend line.
Perhaps there’s a more straight forward means to evaluate this claim. If we performed a linear regression on the years and the heights of the players, it should lead us to an equation that will answer this question. As there are two dates to select from, I believe it’s best to select one or the other as the height is not likely to have changed much, if any during their career. To take the possibility of some change out, I choose to select the ending year.

summary(lm(height~year_end, j))
## 
## Call:
## lm(formula = height ~ year_end, data = j)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -77.437  -2.483   0.134   2.394  12.609 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -33.550543   4.906767  -6.838 9.12e-12 ***
## year_end      0.056111   0.002466  22.750  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.639 on 4548 degrees of freedom
## Multiple R-squared:  0.1022, Adjusted R-squared:  0.102 
## F-statistic: 517.5 on 1 and 4548 DF,  p-value: < 2.2e-16

Height = Year*(.056111) - 33.550543

For 2000: Height = (2000*.05611) -33.550543 = 79.222 inches

Let’s check the correlation:

library(dplyr)

j <- read.csv("/Users/tgarn/OneDrive/Desktop/SMU - MS Data Science/Courses/GitHub/DS_6306_weekly_assignments/Week_2/DS6306_week2/PlayersBBall.csv", header = TRUE)

cor(j$year_end, j$height)
## [1] 0.3196402

This strongly suggests that there is a positive correlation between height and years, from the data provided in the table.

1.8 Bullet 8 - Create a 3D plot of height vs. weight vs. year and color code the points by position.

My thought as I dig into this is, “What could go wrong?”

p <- plot_ly(j, x = ~height, y = ~weight, Z = ~year, color = position) %>% add_markers() %>% layout( scene = list(xaxis = list(title = “Height”), yaxis = list(title = “Weight”), zaxis = list(title = “Year”)))

library(plotly)

j <- read.csv("/Users/tgarn/OneDrive/Desktop/SMU - MS Data Science/Courses/GitHub/DS_6306_weekly_assignments/Week_2/DS6306_week2/PlayersBBall.csv", header = TRUE)

p <- plot_ly(j, x = ~height, y = ~weight, z = ~year_end, color = ~position) %>% add_markers() %>% layout(scene = list(xaxis = list(title = "Height"),yaxis = list(title = "Weight"), zaxis = list(title = "Year")))
p
## Warning: Ignoring 6 observations

1.9 Bullet 9 - Go to this website and use one of the 50 best plots to visualize some aspect of the data and provide at least one insight. You will present your work in breakout: http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.htm

I found 4 of the different visualizations particularly pleasing: 1. Marginal Histogram/Boxplot 2. Treemap 3. Time Series Heat Map 4. Spatial

I’ve seen many of the maps on the web site and the treemap is LOADED with information. I’ve seen that on TV and in magazines. Rich with content. Most people have seen the Spatial maps and they are useful when using GIS or other terrestrial landscape features. I had not seen the Time Series Heat Map, but I truly liked it as it was very illuminating for functions that occur over time and for gleaning recurring patterns. Sales come to mind as does stock and securities trading. The one that I hadn’t seen and believe has the most appeal to the current dataset is the Marginal Histogram/Boxplot map. It provides an added 2 dimensions to otherwise static data. Let me see what I can do with it.

#```{r} #reload BBall CSV j <- read.csv(“/Users/tgarn/OneDrive/Desktop/SMU - MS Data Science/Courses/GitHub/DS_6306_weekly_assignments/Week_2/DS6306_week2/PlayersBBall.csv”, header = TRUE) # load package and data library(ggplot2) #install.packages(‘ggExtra’) library(ggExtra) data(j, package=“ggplot2”) # mpg <- read.csv(“http://goo.gl/uEeRGu”)

2 Scatterplot

theme_set(theme_economist()) # pre-set the bw theme. BBall_select <- j[j\(year_start >= 2011 & j\)height >= 87] g <- ggplot(j, aes(position, height)) + geom_count() + #geom_smooth(method=“lm”, se=F)

#ggMarginal(g, type = “histogram”, fill=“transparent”) ggMarginal(g, type = “boxplot”, fill=“transparent”) #ggMarginal(g, type = “density”, fill=“transparent”) #``` Apparently, not much. I need to attack this again from another angle. Perhaps the Treemap for positions, then count the members of each, and the year? This dataset isn’t perfect for this feature. I’ve literally run out of time. I’m going to take a stab at the very last question with a different dataset EducationIncome.csv. Epic fail.

#```{r} library(ggplot2) #install.packages(‘ggplotify’) library(treemapify) library(ggplotify) proglangs <- read.csv(“/Users/tgarn/OneDrive/Desktop/SMU - MS Data Science/Courses/GitHub/DS_6306_weekly_assignments/Week_2/DS6306_week2/PlayersBBall.csv”, header = TRUE)

3 plot

treeMapCoordinates <- treemapify(proglangs, area = “year_end”, fill = “weight”)

treeMapPlot <- ggplotify(treeMapCoordinates) + scale_x_continuous(expand = c(0, 0)) + scale_y_continuous(expand = c(0, 0)) + scale_fill_brewer(palette = “Dark2”)

print(treeMapPlot) #```

3.1 Last Question - Use the EducationIncome.csv dataset to visually test the claim that the distribution of incomes increase (mean or median) as the education level rises.

First, I’ll load in the dataset and take a look at what we’ve got.

Ed <- read.csv("/Users/tgarn/OneDrive/Desktop/SMU - MS Data Science/Courses/GitHub/DS_6306_weekly_assignments/Week_2/DS6306_week2/Education_Income.csv", header = TRUE)
head(Ed)
##   Subject  Educ Income2005
## 1       2    12       5500
## 2       6    16      65000
## 3       7    12      19000
## 4       8 13-15      36000
## 5       9 13-15      65000
## 6      13    16       8000
#Let's explore how many unique values we have in the Educ column

unique(Ed$Educ, incomparables = FALSE)
## [1] "12"    "16"    "13-15" ">16"   "<12"

Seeing that there are 5 unique timesframes in years, I believe that the most straightforward method, given the remaining time, would be to use these characters to select the values for rising years in education and compare it against Income. I think we can discard the values “<12” and just go with “12” and compare that to “13-15”, “16” and “>16”.

data_12 <- Ed[Ed$Educ == "12",]
#head(data_12)
E12 <- data.frame(data_12$Income2005)
#head(E12)
summary(E12)
##  data_12.Income2005
##  Min.   :   300    
##  1st Qu.: 19975    
##  Median : 31000    
##  Mean   : 36865    
##  3rd Qu.: 48000    
##  Max.   :410008
data_13_15 <- Ed[Ed$Educ == "13-15",]
#head(data_13_15)
E_13_15 <- data.frame(data_13_15$Income2005)
#head(E_13_15)
summary(E_13_15)
##  data_13_15.Income2005
##  Min.   :   429       
##  1st Qu.: 24000       
##  Median : 38000       
##  Mean   : 44876       
##  3rd Qu.: 58000       
##  Max.   :257286
data_16 <- Ed[Ed$Educ == "16",]
#head(data_16)
E_16 <- data.frame(data_16$Income2005)
#head(E_16)
summary(E_16)
##  data_16.Income2005
##  Min.   :   200    
##  1st Qu.: 32000    
##  Median : 56500    
##  Mean   : 69997    
##  3rd Qu.: 89000    
##  Max.   :519340
data_over_16 <- Ed[Ed$Educ == ">16",]
#head(data_over_16)
E_over_16 <- data.frame(data_over_16$Income2005)
#head(E_over_16)
summary(E_over_16)
##  data_over_16.Income2005
##  Min.   :    63         
##  1st Qu.: 40000         
##  Median : 60500         
##  Mean   : 76856         
##  3rd Qu.: 96000         
##  Max.   :703637

The medians show that with rising education there is a strong suggestion that incomes also rise.

*I’m not happy with my work on this Live Set 2 at all. It’s beneath my quality standards. I spent countless hours spinning my wheels. I’ve got to do better in finding solutions. I took shortcuts with the modifying the dataset early on and didn’t have time to come back and change it, even though I figured out how to do so. I’m embarrassed to turn this in. I clearly have a lot of work to do in order to be more efficient with my time. My apologize to the professor for this subpar work.

sessionInfo()
## R version 4.2.2 (2022-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19044)
## 
## Matrix products: default
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## locale:
## [1] LC_COLLATE=English_United States.utf8 
## [2] LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] ggthemes_4.2.4 plotly_4.10.1  dplyr_1.0.10   ggplot2_3.4.0 
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.0   xfun_0.36          bslib_0.4.2        purrr_1.0.0       
##  [5] lattice_0.20-45    splines_4.2.2      colorspace_2.0-3   vctrs_0.5.1       
##  [9] generics_0.1.3     htmltools_0.5.4    viridisLite_0.4.1  yaml_2.3.6        
## [13] mgcv_1.8-41        utf8_1.2.2         rlang_1.0.6        jquerylib_0.1.4   
## [17] pillar_1.8.1       glue_1.6.2         withr_2.5.0        DBI_1.1.3         
## [21] RColorBrewer_1.1-3 lifecycle_1.0.3    stringr_1.5.0      munsell_0.5.0     
## [25] gtable_0.3.1       htmlwidgets_1.6.0  evaluate_0.19      labeling_0.4.2    
## [29] knitr_1.41         fastmap_1.1.0      crosstalk_1.2.0    fansi_1.0.3       
## [33] highr_0.10         scales_1.2.1       cachem_1.0.6       jsonlite_1.8.4    
## [37] farver_2.1.1       digest_0.6.31      stringi_1.7.8      grid_4.2.2        
## [41] cli_3.5.0          tools_4.2.2        magrittr_2.0.3     sass_0.4.4        
## [45] lazyeval_0.2.2     tibble_3.1.8       tidyr_1.2.1        pkgconfig_2.0.3   
## [49] Matrix_1.5-1       ellipsis_0.3.2     data.table_1.14.6  assertthat_0.2.1  
## [53] rmarkdown_2.19     httr_1.4.4         rstudioapi_0.14    R6_2.5.1          
## [57] nlme_3.1-160       compiler_4.2.2